相关实验视频
Updated: Jan 28, 2026

11:07
Focal Laser Ablation of Prostate Cancer: An Office Procedure
Published on: March 30, 2021
8.4K
一个基于人工智能的放射学模型使用MRIADC地图准确预测晚期前列腺癌的进展
Kexin Wang1, Pengsheng Wu2, Yuke Chen3
1Department of Radiology, Peking University First Hospital, Beijing 100034, China.
Current oncology (Toronto, Ont.)
|January 27, 2026
概括
深度学习放射学有效地预测了晚期前列腺癌的进展. 人工智能衍生的瘤细分与手工细分相比,性能相对较好,为风险分层提供同等的临床实用性.
科学领域:
- 放射学 放射学是一门学科.
- 在瘤学瘤学.
- 人工智能的人工智能
背景情况:
- 预测晚期前列腺癌 (PCa) 的进展对于治疗计划至关重要.
- 现有的深度学习放射学模型专注于预测进展,但估计进展时间需要进一步研究.
- 手动瘤细分可能耗时,并且受观察者之间的变化影响.
研究的目的:
- 开发和验证基于深度学习的放射学模型,用于预测使用预处理MR显微扩散系数 (ADC) 地图的高级PCa进展.
- 为了比较手动 (ROIref) 与人工智能衍生 (ROIai) 瘤细分的性能,预测PCa进展.
- 评估人工智能衍生放射学在临床上对风险分层和进展时间估计的有用性.
主要方法:
- 通过使用182名高级PCa患者的预治疗MR ADC地图开发了一种深度学习放射学模型.
- 该模型的有效性通过比较手动 (ROIref) 和AI衍生 (ROIai) 瘤细分来评估.
- 进行了生存分析,包括Cox比例危险回归和决策曲线分析,以评估风险分层和临床效用.
主要成果:
- 深度学习放射学模型实现了PCa进展的高预测性能 (AUC:ROIref=0.840,ROIai=0.852).
- 在AI衍生和手动细分方法之间没有发现显著差异 (ΔAUC = 0.012,p = 0.870).
- ROIref和ROIai预测的概率都独立地预测了进展,并将患者分为不同的生存组 (p < 0.001).
结论:
- 基于深度学习的放射学模型在预测高级PCa进展方面是有效的.
- 人工智能衍生的瘤注释与人工专家细分相比,显示出相当的性能和临床实用性.
- 这种由人工智能驱动的方法为个性化PCa管理和风险分层提供了一个有希望的,高效的工具.
相关概念视频
mTOR Signaling and Cancer Progression
4.7K
The mammalian target of rapamycin or mTOR protein was discovered in 1994 due to its direct interaction with rapamycin. The protein gets its name from a yeast homolog called TOR. The mTOR protein complex in mammalian cells plays a major role in balancing anabolic processes such as the synthesis of proteins, lipids, and nucleotides and catabolic processes, such as autophagy in response to environmental cues, such as availability of nutrients and growth factors.
The mTOR pathway or the...
The mTOR pathway or the...
4.7K
Predicting Molecular Geometry
45.7K
VSEPR Theory for Determination of Electron Pair Geometries
45.7K
Tumor Progression
7.4K
Tumor progression is a phenomenon where the pre-formed tumor acquires successive mutations to become clinically more aggressive and malignant. In the 1950s, Foulds first described the stepwise progression of cancer cells through successive stages.
Colon cancer is one of the best-documented examples of tumor progression. Early mutation in the APC gene in colon cells causes a small growth on the colon wall called a polyp. With time, this polyp grows into a benign, pre-cancerous tumor. Further...
Colon cancer is one of the best-documented examples of tumor progression. Early mutation in the APC gene in colon cells causes a small growth on the colon wall called a polyp. With time, this polyp grows into a benign, pre-cancerous tumor. Further...
7.4K
Prediction Intervals
3.4K
The interval estimate of any variable is known as the prediction interval. It helps decide if a point estimate is dependable.
However, the point estimate is most likely not the exact value of the population parameter, but close to it. After calculating point estimates, we construct interval estimates, called confidence intervals or prediction intervals. This prediction interval comprises a range of values unlike the point estimate and is a better predictor of the observed sample value, y.
However, the point estimate is most likely not the exact value of the population parameter, but close to it. After calculating point estimates, we construct interval estimates, called confidence intervals or prediction intervals. This prediction interval comprises a range of values unlike the point estimate and is a better predictor of the observed sample value, y.
3.4K
Extraction: Advanced Methods
1.1K
Metal ions can be separated from one another by complexation with organic ligands–the chelating agent– to form uncharged chelates. Here, the chelating agent must contain hydrophobic groups and behave as a weak acid, losing a proton to bind with the metal. Since most organic ligands used in this process are insoluble or undergo oxidation in the aqueous phase, the chelating agent is initially added to the organic phase and extracted into the aqueous phase. The metal-ligand complex is...
1.1K
Overview of Advanced Functional Groups
29.7K
Functional groups are groups of atoms with specific chemical properties that occur within organic molecules and are sometimes denoted as “R”. Functional groups can “functionalize” a compound by enabling it to adopt different physical and chemical properties.
Types of Advanced Functional Groups
The table below summarizes some of the major functional groups in organic chemistry.
29.7K

